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sigPCA: Statistical Significance Testing for Principal Components

Identifies principal components whose eigenvalues exceed those expected under noise. Implements analytical thresholds derived from the Marchenko-Pastur distribution (Marchenko and Pastur, 1967) <doi:10.1070/SM1967v001n04ABEH001994> and empirical permutation tests, and provides functions for visualizing observed and null eigenvalue spectra.

Version: 0.1.0
Imports: ggplot2, stats
Suggests: knitr, palmerpenguins, rmarkdown, testthat (≥ 3.0.0)
Published: 2026-08-03
DOI: 10.32614/CRAN.package.sigPCA (may not be active yet)
Author: Guillermo de Anda-Jáuregui [aut, cre, cph], Enrique Hernández-Lemus [aut, cph]
Maintainer: Guillermo de Anda-Jáuregui <gdeanda at inmegen.edu.mx>
BugReports: https://github.com/guillermodeandajauregui/sigPCA/issues
License: MIT + file LICENSE
URL: https://github.com/guillermodeandajauregui/sigPCA
NeedsCompilation: no
Materials: README
CRAN checks: sigPCA results

Documentation:

Reference manual: sigPCA.html , sigPCA.pdf
Vignettes: Finding structure in iris with sigPCA (source, R code)
Finding structure in Palmer penguins with sigPCA (source, R code)
sigPCA: Statistical Significance Testing for Principal Components (source, R code)

Downloads:

Package source: sigPCA_0.1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: sigPCA_0.1.0.zip
macOS binaries: r-release (arm64): sigPCA_0.1.0.tgz, r-oldrel (arm64): not available, r-release (x86_64): sigPCA_0.1.0.tgz, r-oldrel (x86_64): sigPCA_0.1.0.tgz

Linking:

Please use the canonical form https://CRAN.R-project.org/package=sigPCA to link to this page.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.